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> that enables a computer to pretty effectively understand natural language I'd argue that it pretty effectively mimics natural language. I don't think it rea
by lamontcg 10mo ago
> that enables a computer to pretty effectively understand natural language
I'd argue that it pretty effectively mimics natural language. I don't think it really understands anything, it is just the best madlibs generator that the world has ever seen.
For many tasks, this is accurate 99+% of the time, and the failure cases may not matter. Most humans don't perform any better, and arguably regurgitate words without understanding as well.
But if the failure cases matter, then there is no actual understanding and the language the model is generating isn't ever getting "marked to market/reality" because there's no mental world model to check against. That isn't going to be usable if there are real-world consequences of the LLM getting things wrong, and they can wind up making very basic mistakes that humans wouldn't make--because we can innately understand how the world works and aren't always just stringing words together that sound good.
- code_biologist 10mo agoBreak the aspects of language understanding and language generation apart. While I would agree that generative LLMs are understanding-free madlibs for writing text, embedding vector spaces and LLM latent spaces seem are a pretty genuine understanding of natural language. High dimensional vector spaces seem like the best machine representation we currently have for meaning and LLMs are using it effectively.